In this work, we carried out the design and synthesis of new chimeric compounds from the natural cytotoxic chalcone 2',4'-dihydroxychalcone (2',4'-DHC, ) in combination with cinnamic acids. For this purpose, a descriptive and predictive quantitative structure-activity relationship (QSAR) model was developed to study the chimeric compounds' anti-cancer activities against human breast cancer MCF-7, relying on the presence or absence of structural motifs in the chalcone structure, like in a Free-Wilson approach. For this, we used 207 chalcone derivatives with a great variety of structural modifications over the α and β rings, such as halogens (F, Cl, and Br), heterocyclic rings (piperazine, piperidine, pyridine, etc.), and hydroxyl and methoxy groups. The multilinear equation was obtained by the genetic algorithm technique, using logIC as a dependent variable and molecular descriptors (constitutional, topological, functional group count, atom-centered fragments, and molecular properties) as independent variables, with acceptable statistical parameter values (R2 = 86.93, Q2 = 82.578, Q2 = 80.436, and Q2 = 80.226), which supports the predictive ability of the model. Considering the aromatic and planar nature of the chalcone and cinnamic acid cores, a structural-specific QSAR model was developed by incorporating geometrical descriptors into the previous general QSAR model, again, with acceptable parameters (R2 = 85.554, Q2 = 80.534, Q2 = 78.186, and Q2 = 79.41). Employing this new QSAR model over the natural parent chalcone 2',4'-DHC () and the chimeric compound 2'-hydroxy,4'-cinnamate chalcone (), the predicted cytotoxic activity was achieved with values of 55.95 and 17.86 µM, respectively. Therefore, to corroborate the predicted cytotoxic activity compounds and were synthesized by two- and three-step reactions. The structures were confirmed by H and C NMR and ESI+MS analysis and further evaluated in vitro against HepG2, Hep3B (liver), A-549 (lung), MCF-7 (breast), and CasKi (cervical) human cancer cell lines. The results showed IC values of 11.89, 10.27, 56.75, 14.86, and 29.72 µM, respectively, for the chimeric cinnamate chalcone . Finally, we employed as a molecular scaffold for the generation of cinnamate candidates (-), which incorporated structural motifs that enhance the cytotoxic activity (pyridine ring, halogens, and methoxy groups) according to our QSAR model. ADME/tox in silico analysis showed that the synthesized compounds and , as well as the proposed chalcones and , are the best candidates with adequate drug-likeness properties. From all these results, we propose (as a molecular scaffold) and our two QSAR models as reliable tools for the generation of anti-cancer compounds over the MCF-7 cell line.
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http://dx.doi.org/10.3390/molecules28145486 | DOI Listing |
Drug Des Devel Ther
January 2025
Department of Trauma Orthopedics, Affiliated Hospital of Jining Medical University, Jining, Shandong, 272007, People's Republic of China.
Purpose: Osteosarcoma (OS) is the most common malignant tumor associated with poor patient outcomes and a limited availability of therapeutic agents. Scutellarein (SCU) is a monomeric flavone bioactive compound with potent anti-cancer activity. However, the effects and mechanisms of SCU on the growth of OS remain unknown.
View Article and Find Full Text PDFHeliyon
January 2025
Department of Mathematics, Faculty of Sciences, Ghazi University, Dera Ghazi Khan, 32200, Pakistan.
Chemical structures may be defined based on their topology, which allows for the organization of molecules and the representation of new structures with specific properties. We use topological indices, which are precise numerical measurements independent of structure, to measure the bonding arrangement of a chemical network. An essential objective of studying topological indices is to collect and alter chemical structure data to develop a mathematical relationship between structures and physico-chemical properties, bio-activities, and associated experimental factors.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Mathematical Sciences, Faculty of Science, Somali National University, Mogadishu Campus, Mogadishu, Somalia.
In recent years, machine learning has gained substantial attention for its ability to predict complex chemical and biological properties, including those of pharmaceutical compounds. This study proposes a machine learning-based quantitative structure-property relationship (QSPR) model for predicting the physicochemical properties of anti-arrhythmia drugs using topological descriptors. Anti-arrhythmic drug development is challenging due to the complex relationship between chemical structure and drug efficacy.
View Article and Find Full Text PDFEnviron Toxicol Chem
January 2025
School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, PR China.
In silico methods are increasingly important in predicting the ecotoxicity of engineered nanomaterials (ENMs), encompassing both individual and mixture toxicity predictions. It is widely recognized that ENMs trigger oxidative stress effects by generating intracellular reactive oxygen species (ROS), serving as a key mechanism in their cytotoxicity studies. However, existing in silico methods still face significant challenges in predicting the oxidative stress effects induced by ENMs.
View Article and Find Full Text PDFMol Inform
January 2025
Department of Applied Chemistry, School of Science and Technology, Meiji University, 1-1-1 Higashi-Mita, Tama-ku, Kawasaki, Kanagawa 214-8571, Japan.
Recent advances in machine learning have significantly impacted molecular design, notably the molecular generation method combining the chemical variational autoencoder (VAE) with Gaussian mixture regression (GMR). In this method, a mathematical model is constructed with X as the latent variable of the molecule and Y as the target properties and activities. Through direct inverse analysis of this model, it is possible to generate molecules with the desired target properties.
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